Network Input. The input \(\mathrm {W}_{i}\)  is reshaped into a \(P \times 2 \times 1\)  feature tensor which can be fed into convolution layers. Here, the number of channels is \(P\) , the width is 2 and the height is 1. We empirically find that it works better than vectorizing \(\mathrm {W}_{i}\)  into \(2P \times 1 \times 1\)  like in [1]}, [2]}.
